Object tracking has been widely used in real-world applications such as traffic monitoring, security surveillance, and so forth. In object tracking, an object of interest is usually bounded with a rectangle window, and the parameters of the window (which include window location, size and shape) are estimated for each picture in a video sequence. Generally for a real-world application, object tracking should achieve a fine balance between accuracy, robustness and limited bandwidth (e.g., across a camera network). However, conventional methods directed to object tracking typically cannot handle the problem of a time-varying foreground/background.
The wide range of objects to be tracked poses a challenge to any object tracking application. Different object and feature representations, such as color histograms, appearance models, and key-points, have been used for object tracking. In one conventional approach, feature selection involves the use a set of different feature spaces and “switching” to the most discriminative features.
Object tracking can be considered as a classification problem. The tracker continuously updates the current classifier that represents the object to optimally discriminate the object from the current background.
The tracker builds an initial classifier using the object region as positive samples and patches in the local neighborhood of the object region as negative samples. The initial object region is determined by a user or object detection. During the updating, the classifier is evaluated in a region of interest around the previous position. Conventional classifier based methods update the strong classifiers by adding and deleting weak classifiers over time to cope with scene changes.
Thus, conventional tracking methods have at least two significant problems, namely a time-varying scale and track deviation.
Regarding the time-varying scale, in a video sequence, the size of the object of interest usually changes according to its position, pose and distance to the camera. To determine the correct size of the bounding window, conventional tracking methods test window sizes from small to large, and find an optimal window size according to some criteria. This process is very time-consuming and sensitive to perturbation from the environment. For those tracking methods that employ a classification technique, the correct window size is vital. An improper window size will significantly influence the classification performance as well as deteriorate the final tracking performance.
Regarding track deviation, in a tracking process, the bounding box or tracking window can significantly deviate from the object of interest due to perturbation or noise. Since these problems are not well addressed by conventional tracking methods, there is a need for improved methods.